Knowledge mapping of ferroptosis in sarcoma: a bibliometric and bioinformatics analysis (2012-2023)

Abstract

Background: Sarcoma is a rare and heterogeneous group of malignant tumors originating from mesenchymal tissues, which presents significant challenges for diagnosis and treatment. Ferroptosis, a newly recognized form of iron-dependent cell death, is distinct from other cell death mechanisms such as apoptosis and autophagy. Recent studies have shown that the induction of ferroptosis is an effective way to kill sarcoma cells and reduce their resistance to chemotherapeutic drugs, highlighting the importance of understanding how ferroptosis may influence the biology and treatment of sarcomas.

Methods: In this study, we employed three main methods, namely CiteSpace, VOSviewer, and the R package "bibliometrix", to analyze relevant literature. Publications related to ferroptosis and sarcoma in the Science Citation Index Expanded of the Web of Science Core Collection (WoSCC) database (2012-2023) were included, and bioinformatics analyses were performed using R Studio and public databases.

Results: The analysis revealed that research on sarcomas and ferroptosis has experienced a steady increase over the years, with a diverse range of research topics and collaborations established among researchers worldwide. The key findings include the identification of influential authors and institutions, prominent research clusters, and emerging research trends. The bioinformatics analysis results confirmed the significance of ferroptosis-related gene ACSF2 in different sarcomas. Notably, the scarcity of studies focusing on the relationship between sarcoma and ferroptosis has been observed, highlighting the potential for further exploration in this area.

Conclusion: The integration of bibliometrics and bioinformatics provides valuable insights into the research landscape of sarcoma and ferroptosis. Future research on ferroptosis will continue to focus on its mechanisms in sarcomas including immune microenvironment, while also further exploring its potential clinical applications. We have identified a potential ferroptosis-related gene, ACSF2, which shows associations with survival in sarcoma datasets. Further testing is needed to validate its potential as a prognostic biomarker.

Keywords: Bibliometrics; Bioinformatics; Ferroptosis; Sarcoma; Tumors.

Fig. 1

The flowchart of publications screening

Fig. 2

Annual output of research of ferroptosis in sarcoma

Fig. 3

The geographical distribution (A) and visualization of countries (B) on research of ferroptosis in sarcoma

Fig. 4

The visualization of institutions on research of ferroptosis in sarcoma

Fig. 5

The visualization of journals (A) and co-cited journals (B) on research of ferroptosis in sarcoma

Fig. 6

The visualization of authors (A) and co-cited authors (B) on research of ferroptosis in sarcoma

Fig. 7

The visualization of co-cited references on research of ferroptosis in sarcoma

Fig. 8

Top references with strong citation bursts. A red bar indicates high citations in that year

Fig. 9

Keyword cluster analysis A and trend topic analysis B

Fig. 10

A Volcano plot showing differentially expressed genes (DEGs) between tumor and normal sarcoma tissues. Red dots represent upregulated genes; blue dots represent downregulated genes. B Venn diagram showing the overlap between DEGs and ferroptosis-related genes from FerrDb. C GO and KEGG pathway enrichment analysis of overlapping genes, highlighting metabolic and redox-related pathways

Fig. 11

The survival curve shows the distinct OS between low and high risk groups. The blue line represents the low-expression group, while the red line represents the high-expression group. The hazard ratio (HR) along with its 95% confidence interval and the p value from the log-rank test are presented. The p value is used to evaluate the statistical significance of the survival differences between the two groups

Fig. 12

A, B Correlations between genes MIOX, ACSF2 and diverse immune cell types. The x-axis represents the correlation coefficient, and the y-axis lists immune cell types. Dot color indicates p value significance, and dot size represents the absolute value of the correlation coefficient. C, D PPI network centered on ACSF2 and MIOX. Nodes represent proteins or genes, and lines indicate interactions, revealing functional connections

Fig. 13

Volcano plot of GSE2719 and GSE6481 (A). Volcano plot of GSE17679 (B). Volcano plot of GSE14359 (C). Differential expression of different sarcomas and control (D). Differential expression of Ewing sarcomas and control (E). Differential expression of osteosarcomas with lung metastasis and primary osteosarcoma (F). Red dots represent upregulated genes; blue dots represent downregulated genes in volcano plot. The statistical symbols (** for p < 0.01, *** for p < 0.001) indicate a significant difference between the two groups